A novel diagnostic information based framework for super-resolution of retinal fundus images

• Published in: Computerized medical imaging and graphics 2019-03, Vol.72, p.22-33
• Main Authors: Das, Vineeta, Dandapat, Samarendra, Bora, Prabin Kumar
• Format: Article
• Language: English
• Subjects: Algorithms; Bicubic interpolation; Calibration; Classification; Computational efficiency; Computer applications; Computing time; Contrast sensitive function; Diabetes mellitus; Diabetic retinopathy; Diagnostic systems; Digital imaging; Discrete wavelet transform; Entropy (Information theory); Fundus image; Image classification; Image reconstruction; Image resolution; Interpolation; Medical diagnosis; Medical imaging; Portable equipment; Retina; Retinal images; Retinopathy; Shannon entropy; Signal to noise ratio; Sparse representation; Super-resolution; Support vector machine (SVM); Support vector machines; Telemedicine
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2019.01.002

•To the best of our knowledge, the problem of resolution enhancement of the retinal images has not been addressed.•The proposed method takes into consideration the diagnostic information in the retinal fundus image during super-resolution (SR).•The method performs SR only on the zone of interest rather than the entire image. This leads to computational time efficiency.•A set of novel fundus image specific features are extracted to classify the diagnostically significant and non-significant zones.•A trade-off between the learning based method and bicubic interpolation is performed during SR which leads to achievement in computational time without loss in reconstruction accuracy. Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images. Clinical information of the selected zone is captured using the Shannon entropy, the contrast sensitivity index (CSI), the multi-resolution (MR) intra-band energy and the MR inter-band eigen features. The support vector machine (SVM) classifier is used to decide the clinical significance of the zone. Highly accurate learning based SR method or the bicubic interpolation is applied to the selected zone based on the classification output. The method is tested on the Standard Diabetic Retinopathy Database Calibration level 1 (DIARETDB1) and the Digital Retinal Images for Vessel Extraction (DRIVE) databases. Classification accuracy of 85.22% and 85.77% is achieved for the DIARETDB1 and DRIVE databases respectively. The SR performance of the algorithm is quantified in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and computational time. The proposed diagnostic information based SR achieves computational time efficiency without compromising with the high resolution (HR) reconstruction accuracy of the fundus image zones.